7 resultados para Computer industry

em CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal


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Experimental scratch resistance testing provides two numbers: the penetration depth Rp and the healing depth Rh. In molecular dynamics computer simulations, we create a material consisting of N statistical chain segments by polymerization; a reinforcing phase can be included. Then we simulate the movement of an indenter and response of the segments during X time steps. Each segment at each time step has three Cartesian coordinates of position and three of momentum. We describe methods of visualization of results based on a record of 6NX coordinates. We obtain a continuous dependence on time t of positions of each of the segments on the path of the indenter. Scratch resistance at a given location can be connected to spatial structures of individual polymeric chains.

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The objective of this paper is to provide empirical evidence on the determinants of gender wage inequality in the Portuguese tourism industry. Relying on firm level wage equations and production functions, gender wage and productivity differentials are estimated and then compared in order to infer whether observed gender disparities are justifiable on the grounds that women are relatively less productive than men, or instead disparities are due to gender wage discrimination. This approach is applied to tourism industry data gathered in the matched employer-employee data set Quadros de Pessoal (Employee Records). The main findings indicate that female employees in the tourism industry in Portugal are less productive than their male colleagues and that gender differences in wages are fully explained by gender differences in productivity.

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Tourism represents a major economic activity in Portugal, with an enormous wealth and employment growth potential. A significant proportion of jobs in the industry tourism are occupied by women, given that this industry is characterized by a relatively higher percentage of female employees. Despite the evidence of female progress with regard to their role in the Portuguese labor market, women continue to earn less than their male counterparts. This is clearly the case of the tourism industry, where statistics reveal a persistent gender wage gap. The objective of this paper is to provide empirical evidence on the determinants of gender wage inequality in the tourism industry in northern Portugal. Relying on firm-level wage equations and production functions, gender wage and productivity differentials are estimated and then compared. The comparison of these differentials allows inferring whether observed wage disparities are attributable to relatively lower female productivity, or instead disparities are due to gender wage discrimination. This approach is applied to tourism industry data gathered in the matched employer-employee data set Quadros de Pessoal (Employee Records). The main findings indicate that female employees in the tourism industry in northern Portugal are less productive than their male colleagues and that gender differences in wages are fully explained by gender differences in productivity.

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The success of the digital game industry is spawning several undergraduate degrees aiming at the training of digital game developers. Building adequate new courses curricula is not a trivial task and demands a profound analysis of the scientific areas to introduce as well as the dependencies throughout the entire degree. Another important aspect of every academic educational plan are the satellite projects that promote entrepreneurship and provide practical professional experiences to students. This paper presents the main guidelines adopted in the creation of the first digital game development undergraduate degree created in Portugal.

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Abstract: in Portugal, and in much of the legal systems of Europe, «legal persons» are likely to be criminally responsibilities also for cybercrimes. Like for example the following crimes: «false information»; «damage on other programs or computer data»; «computer-software sabotage»; «illegitimate access»; «unlawful interception» and «illegitimate reproduction of protected program». However, in Portugal, have many exceptions. Exceptions to the «question of criminal liability» of «legal persons». Some «legal persons» can not be blamed for cybercrime. The legislature did not leave! These «legal persons» are v.g. the following («public entities»): legal persons under public law, which include the public business entities; entities utilities, regardless of ownership; or other legal persons exercising public powers. In other words, and again as an example, a Portuguese public university or a private concessionaire of a public service in Portugal, can not commit (in Portugal) any one of cybercrime pointed. Fair? Unfair. All laws should provide that all legal persons can commit cybercrimes. PS: resumo do artigo em inglês.

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Nowadays despite improvements in usability and intuitiveness users have to adapt to the proposed systems to satisfy their needs. For instance, they must learn how to achieve tasks, how to interact with the system, and fulfill system's specifications. This paper proposes an approach to improve this situation enabling graphical user interface redefinition through virtualization and computer vision with the aim of increasing the system's usability. To achieve this goal the approach is based on enriched task models, virtualization and picture-driven computing.

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Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.